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2020 – today
- 2025
[j2]Ari Pakman
:
Super-efficient exact Hamiltonian Monte Carlo for the von Mises distribution. Appl. Math. Lett. 159: 109284 (2025)- 2024
- [i7]Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman:
von Mises Quasi-Processes for Bayesian Circular Regression. CoRR abs/2406.13151 (2024) - 2021
- [c9]Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman:
Estimating the Unique Information of Continuous Variables. NeurIPS 2021: 20295-20307 - [i6]Ari Pakman, Dar Gilboa, Elad Schneidman:
Estimating the Unique Information of Continuous Variables. CoRR abs/2102.00218 (2021) - [i5]Dar Gilboa, Ari Pakman, Thibault Vatter:
Marginalizable Density Models. CoRR abs/2106.04741 (2021) - 2020
- [c8]Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski:
Neural Clustering Processes. ICML 2020: 7455-7465 - [i4]Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski:
Attentive Clustering Processes. CoRR abs/2010.15727 (2020)
2010 – 2019
- 2019
- [c7]Ari Pakman, Yueqi Wang, Liam Paninski:
Neural Permutation Processes. AABI 2019: 1-7 - [i3]Ari Pakman, Liam Paninski:
Discrete Neural Processes. CoRR abs/1901.00409 (2019) - 2018
- [i2]Ari Pakman, Liam Paninski:
Amortized Bayesian inference for clustering models. CoRR abs/1811.09747 (2018) - 2017
- [c6]Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski:
Stochastic Bouncy Particle Sampler. ICML 2017: 2741-2750 - 2016
- [c5]David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski:
Partition Functions from Rao-Blackwellized Tempered Sampling. ICML 2016: 2896-2905 - [c4]Roy Fox, Ari Pakman, Naftali Tishby:
Taming the Noise in Reinforcement Learning via Soft Updates. UAI 2016 - 2015
- [i1]Roy Fox, Ari Pakman, Naftali Tishby:
G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates. CoRR abs/1512.08562 (2015) - 2014
- [j1]Ari Pakman, Jonathan Hunter Huggins, Carl Smith, Liam Paninski:
Fast state-space methods for inferring dendritic synaptic connectivity. J. Comput. Neurosci. 36(3): 415-443 (2014) - 2013
- [c3]Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski:
Bayesian spike inference from calcium imaging data. ACSSC 2013: 349-353 - [c2]Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski:
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS 2013: 1304-1312 - [c1]Ari Pakman, Liam Paninski:
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. NIPS 2013: 2490-2498
Coauthor Index
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